Effect of Inoculation with Lentilactobacillus buchneri and Lacticaseibacillus paracasei on the Maize Silage Volatilome: The Advantages of Advanced 2D-Chromatographic Fingerprinting Approaches

In this study, the complex volatilome of maize silage samples conserved for 229 d, inoculated with Lentilactobacillus buchneri (Lbuc) and Lacticaseibacillus paracasei (Lpar), is explored by means of advanced fingerprinting methodologies based on comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry. The combined untargeted and targeted (UT) fingerprinting strategy covers 452 features, 269 of which were putatively identified and assigned within their characteristic classes. The high amounts of short-chain free fatty acids and alcohols were produced by fermentation and led to a large number of esters. The impact of Lbuc fermentation was not clearly distinguishable from the control samples; however, Lpar had a strong and distinctive signature that was dominated by propionic acid and 1-propanol characteristic volatiles. The approach provides a better understanding of silage stabilization mechanisms against the degradative action of yeasts and molds during the exposure of silage to air.


■ INTRODUCTION
Improving silage fermentation and aerobic stability through the use of bacterial inocula is a widely studied area. 1 The aerobic stability of silage during the feed-out phase is one of the most frequently requested characteristics of silage at a farm level to reduce the risk of aerobic deterioration. 2 Among the lactic acid bacteria (LAB) used to improve the aerobic stability of silages, heterofermentative Lentilactobacillus buchneri strains are the most successful. 1,3 Their action arises from their capability of modifying fermentative patterns by partially converting lactic acid into acetic acid and 1,2-propanediol. 4 Several studies have only attributed the improvement of aerobic stability from the use of Len. buchneri to an increase in acetic acid, 3,5 even though, in several cases, the acetic acid content has not been able to fully explain the yeast reduction and the increase in aerobic stability. 6 Silage fermentation is a complex process, and a large number of compounds is generated. 7 Silage volatile organic compounds (VOCs) represent a complex fraction that includes both native components of unfermented silage, derived from primary and secondary/specialized plant metabolisms, and volatile metabolites produced by the metabolic activity of bacteria and yeasts during fermentation. 8 Moreover, certain abiotic conditions (pH and temperature) can promote the formation of additional components, as in the case of ester derivatives. 9 The investigation of silage VOCs was first reported by Krizsan et al. (2007) who identified and quantified 13 esters, five aldehydes, three alcohols, and one sulfur derivative that showed effects on the voluntary intake of growing steers. The role of silages in contributing to the atmospheric emission of VOCs was then studied. 9,10 Around 80 compounds of acids, ketones, aldehydes, alcohols, esters, and other groups were identified in maize, alfalfa, and cereal silages. 10 The use of comprehensive two-dimensional (2D) gas chromatography (GC × GC) coupled with time-of-flight mass spectrometry (TOF MS), can greatly improve the knowledge about the quali-/quantitative composition of silage VOCs and add further information to better understand the complex phenomena behind aerobic stability, bacteria and yeast metabolic activity, synergies, and cross-interactions. The improved separation power of GC × GC, compared to onedimensional (1D) GC, accompanied by the logical retention patterns of chemically related compounds, and specialized data processing techniques, make GC × GC-TOF MS the most suitable platform for an accurate exploration of complex volatile fractions (i.e., the volatilome). 11−13 When the fraction under study poses challenges, because of the large dynamic range of concentrations, and consists of analytes with a wide polarity range within a relatively narrow volatility interval, chromatographic resolution and efficiency are fundamental to achieve appropriate performances. Moreover, when an investigation is directed toward all the detectable components, untargeted approaches represent the ideal strategy, since they are not biased by previous knowledge of specific markers or target analytes and lead to a comprehensive understanding of the phenomena.
In this study, state-of-the art GC × GC-TOFMS, combined with automated headspace solid-phase microextraction (HS-SPME), has been adopted for the first time to capture the complexity and chemical dimensionality of the maize silage volatilome. The effects of silage inocula, based on commercial strains of Len. buchneri and a new strain of Lacticaseibacillus paracasei, on the volatilome of maize silage harvested at two different dry matter (DM) contents, have been studied to investigate the role of some VOCs in improving aerobic stability after silo opening. Moreover, in order to take a step forward in the understanding of the biological phenomena behind silage fermentation, comprehensive chromatographic fingerprinting, 14 covering untargeted and targeted components, has been applied. The chemical signatures of the volatilome have been explored with unsupervised and supervised chemometrics to highlight the interaction of Len. buchneri and Lcb. paracasei with the epiphytic microorganisms present on the forage at harvesting.

■ MATERIALS AND METHODS
Chemicals. The pure α/β-thujone and methyl 2-octynoate reference standards, used as internal standards (ISs), the n-alkanes (from n-C9 to n-C25), used for linear retention indice (I T s) calibration, and the solvents (cyclohexane, toluene, and dibutyl phthalate�99% of purity) used in the analyses were all obtained from Merck.
Fermented Maize Silage Samples. The trial was performed at the experimental farm of the University of Turin in the western Po plain, northern Italy (44°53′N, 7°41′E, altitude 232 m a.s.l.). Maize (P1547W, Pioneer Hi-Bred Italia Srl) was seeded on two different dates (2020-04-16 and 2020-05-25) in order to contemporary harvest two whole crops with different DM contents [LOW (32% DM) and HIGH (39% DM)]. The forage was directly harvested as a chopped whole crop using a precision forage harvester (Claas Jaguar 970 equipped with a New Holland 350W forage harvester head) at a 15 mm chopping length. The field was divided into three blocks. The chopped material from each block was divided into two representative 80 kg piles (one for each treatment) for each DM content. The piles were either not treated, and used as a negative control (CON), or treated with Lentilactobacillus buchneri and Lactiplantibacillus plantarum (Corteva Agriscience, Johnston, Iowa, USA) at a theoretical application rate of 1.1 × 10 5 cfu g −1 fresh matter FM (Lbuc) or Lacticaseibacillus paracasei (UNITO 012, University of Turin, Italy) at a theoretical application rate of 1 × 10 6 cfu g −1 FM (Lpar). A hand sprayer was used to uniformly spray the inocula onto the forage, which was continuously hand-mixed. The fresh forages were sampled (one sample from each pile) prior to ensiling and after treatment with the inocula. The forages were hand-packed into 20 L plastic silos equipped with a lid that only enabled gas release, and the final average packing density was 674 ± 31 and 584 ± 32 kg FM m −3 , for LOW and HIGH, respectively. All the laboratory silos were filled within 3 h. The silos were weighed, conserved at ambient temperature (20 ± 1°C), and opened after 229 d of anaerobic conservation. At opening, each silo was again weighed, and the content was mixed thoroughly and subsampled to determine the DM content, chemical composition, fermentation profile, microbial counts, and aerobic stability.
The weight losses due to fermentation were calculated as the difference between the weight of the forage placed in each plastic silos at ensiling and the weight of the silage at the end of conservation, and they were expressed as the percentage of the amount of DM ensiled in each plastic silo.
After sampling, the silages were subjected to an aerobic stability test. Aerobic stability was determined by monitoring the temperature increases due to the microbial activity of the samples exposed to air. About three kilograms of each silo was allowed to aerobically deteriorate in a controlled temperature room (20 ± 1°C) in 17 L polystyrene boxes (290 mm diameter and 260 mm height) for 14 d. A single layer of aluminum cooking foil was placed over each box to prevent drying and dust contamination but also to allow air to penetrate. The temperatures of the room and of the silage were measured each hour by a data logger. Aerobic stability was defined as the number of hours the silage remained stable before rising more than 2°C above room temperature as reported by Kleinschmit and Kung. 3 Sample Preparation and Analyses. Each of the pre-ensiled herbages and the silages were split into five subsamples of about 500 g.
The first subsample was analyzed immediately, for the DM content, by oven drying at 80°C for 24 h. The dry matter was corrected, according to Porter and Murray,15 to consider the volatile compound losses that can take place at 80°C.
The second subsample was used to determine the water activity (a w ), pH, nitrate (NO 3 ), and the buffering capacity. The water activity was measured at 25°C on a fresh sample using an AquaLab Series 3TE (Decagon Devices Inc.), which adopts the chilled-mirror dew point technique. The fresh forage was extracted for pH and nitrate determination, using a Stomacher blender (Seward Ltd.), for 4 min in distilled water at a 9:1 water-to-sample material (fresh weight) ratio. The total nitrate concentration was determined in the water extract, through semiquantitative analysis, using Merckoquant test strips (Merck; detection limit 100 mg NO 3 kg −1 DM). The pH was determined using a specific electrode (DL21 Titrator, Mettler Toledo, with electrode Liq-Glass 238000, Hamilton, Agrate Brianza, IT). The buffering capacity was determined in the water extract, as described by Playne and McDonald. 16 A third fresh subsample was extracted, using a Stomacher blender, for 4 min in H 2 SO 4 0.05 mol L −1 at a 4:1 acid-to-sample material (fresh weight) ratio. An aliquot of 40 mL of silage acid extract was filtered with a 0.20 μm syringe filter and used for quantification of the fermentation products. The lactic and monocarboxylic acids (acetic, propionic, and butyric acids) in the acid extract were determined using high-performance liquid chromatography (HPLC, Agilent Technologies, 1200 Series). 17 Ethanol and 1,2-propanediol were determined using HPLC, coupled with a refractive index detector, on a Aminex HPX-87H column (Bio-Rad Laboratories).
The fourth fresh subsample was used for the microbial analyses. In order to conduct the microbial counts, a 30 g sample was transferred to a sterile homogenization bag, suspended 1:9 w/v in a peptone salt solution (1 g of bacteriological peptone and 9 g of sodium chloride per liter), and homogenized for 4 min in a laboratory Stomacher blender (Seward Ltd.). Serial dilutions were prepared, and the yeast and mold numbers were determined using the pour plate technique with 40.0 g L −1 of Yeast Extract Glucose Chloramphenicol Agar (YGC agar, DIFCO) after incubation at 25°C for 3 and 5 d for yeast and mold, respectively. The yeast and mold colony forming units (cfu) were enumerated separately, according to their macromorphological features, on plates that yielded 1−100 cfu. The LAB were determined on MRS agar with added natamycin (0.25 g L −1 ), by incubating the Petri plates at 30°C for 3 d in anaerobic jars with a gas generating system (AnaeroGenTM, Thermo Fisher Scientific). Since LAB are facultative anaerobe bacteria, anaerobic incubation was chosen to improve the selectivity of the media against Bacillus spp.
The fifth fresh subsample, used for volatilome analysis, was stored in a plastic, phthalate-free container, immediately frozen at −80°C, and kept refrigerated until analysis.
The main chemical and microbial characteristics of whole crop corn (WCC), harvested at LOW and HIGH DM contents prior to ensiling and after 229 d of fermentation, are reported in Table 1.
Headspace Solid-Phase Microextraction: Devices and Conditions. The volatile organic compounds from the silage samples were sampled by means of HS-SPME using an SPR Auto sampler for GC (SepSolve-Analytical). A divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (d f 50/30 μm; 2 cm length) from Merck was chosen because of the possibility of combining sorption and adsorption mechanisms with components covering a large polarity range. The SPME fiber was conditioned before use, as recommended by the manufacturer. The ISs (α/β-thujone and methyl 2-octynoate) were preloaded onto the SPME device 18,19 by sampling from a 20 mL headspace vial containing a 5.0 μL aliquot of the ISs solution (100 mg L −1 ) prepared in dibutyl phthalate as a solvent. The ISs were equilibrated at 40°C, and the SPME device was exposed to the ISs HS for 5 min. ISs were used for validation purposes (method precision and repeatability) and to normalize the analytes' absolute responses (i.e., % normalized response).
Sampling was carried out on 1.00 ± 0.10 g of finely ground silage, precisely weighed, placed in 20 mL headspace vials, and kept at 40°C for 50 min under constant agitation. The amount of the sample, the sampling temperature, and time were optimized after preliminary experiments (data not shown). The final conditions were set to obtain the maximum extraction efficiency in a reasonable sampling time (according to the duration of the analytical run) and at a temperature where the formation of artifacts and side-reactions was minimized (i.e., 40°C).
After extraction, the SPME device was automatically transferred to the split/splitless injection port of the GC × GC system and kept at 250°C, and thermal desorption was then run for 5 min. The samples were analyzed in duplicate and randomly distributed over one week of measurements.
GC × GC-TOF MS with Loop-type Thermal Modulation: Instrument Setup and Conditions. Comprehensive two-dimensional GC analyses were carried out with an Agilent 7890B GC chromatograph (Agilent Technologies) coupled with a Markes BenchTOF Select mass spectrometer featuring tandem ionization (Markes International). The GC transfer line was set at 270°C. The TOF MS was tuned for single ionization at 70 eV, and the scan range was set between 35 and 350 m/z with a spectrum acquisition frequency of 100 Hz. The thermal modulator was a loop-type, twostage KT 2004 (Zoex Corporation) cooled with liquid nitrogen and controlled by Optimode, v2.0 (SRA Intruments, Cernusco sul Naviglio). The modulation period (P M ) was set at 3.5 s, while the hot-jet pulse duration was set at 250 ms. The cold-jet stream at the mass flow controller (MFC) was programmed to linearly reduce the total flow (i.e., 20 L/min) from 40% to 8% along the analytical run.
The column set consisted of a 1 D HeavyWax column (100% poly The GC split/splitless injector port was set at 250°C and operated in pulsed-split mode (250 kPa overpressure applied to the injection port until 2 min) with a 1:20 split ratio. A special design liner for SPME thermal-desorption (Merck) was used to improve the transfer of the analytes to the 1 D column and to limit band broadening inspace. Helium was used as the carrier gas at a nominal flow of 1.3 mL/ min. The oven temperature program was set as follows: from 40°C (2 min) to 240°C (10 min) at 3.5°C min −1 .
The n-alkanes solution for I T s determination was analyzed under the following conditions: split/splitless injector in split mode, 1:50 split ratio, 250°C injector temperature, and 1 μL injection volume.
HS-SPME-GC × GC-TOF MS Method Performance Parameters. The performance parameters of the method were evaluated to assess the repeatability for the retention times ( 1 t R and 2 t R over one-week) and for the 2D peak response indicators (i.e., absolute responses −2D peak volumes and % normalized responses over ISs−2D peak percent response). The % relative standard deviation (%RSD) was therefore calculated for retention indicators on all the targeted and untargeted components (UT features n = 452) for all the analyses run over a one week time frame (n = 35). The obtained results are reported in Supporting Information Table 1, together with the average retention times in the two chromatographic dimensions, the calculated retention indices (I T ), and the tabulated values, according to the NIST database (NIST Standard Reference Database, 2005). 20 The mean %RSD of the retention times was 0.79% for the 1 D ( 1 t R ) and     Table 1 for the targeted peaks (n = 269) and untargeted features (n = 183). The "-" symbol in the table refers to those features that were not matched in the QC samples. The maximum %RSD (% RSD QC) of the absolute response was 36%, as reported for heptanoic acid, and the mean was 12.5 %. Untargeted Targeted Fingerprinting by Pattern Recognition. Applying a data elaboration workflow to GC × GC-TOF MS data enables the peak and peak-region features to be captured from the untargeted and targeted components separated on the 2D retentiontime plane. Such an approach is named UT fingerprinting. 21,22 In this application, analyte targeting (i.e., identification) was performed as the last step, before data mining. A schematic workflow of the UT fingerprinting strategy is reported in reference literature by  The strategy used to generate and realign untargeted features (i.e., peaks and peak-regions) across all the chromatograms is known as template matching. 25 This process is performed as a first step of the processing workflow. Metadata (retention times, MS fragmentation patterns, and detector responses) are extracted for 2D peaks and peak-regions; those that exceed a signal-to-noise (S/N) threshold value of 100 are used to establish correspondences across multiple chromatograms (n = 35) and to coherently realign them. Constraints are applied to validate positive matches between chemical entities in order to achieve adequate specificity. 11,26 A spectral-similarity threshold of 750 was defined for the direct match factor (DMF) and reverse match factor (RMF) between the template (reference) and candidate (analyzed) MS signatures according to the NIST MS Search algorithm, ver. 2.0 (National Institute of Standards and Technology). 26 The "peak spectra", that is, the average MS signature from the largest data point within the 2D peak, was used for spectral matching.
The results of the fingerprinting are tables in which the 2D peaks and/or peak-regions are aligned across all the chromatograms with their-related metadata (e.g., 1 D and 2 D retention times − 1 t R and 2 t R , MS fragmentation pattern, base peak and molecular ion m/z, and TIC response).
In this study, the process aligned the 35 acquired chromatograms using reliable peaks for registration and generated a composite chromatogram from which the peak-region features were delineated and extracted into a template that was used for further chromatogram processing. By applying constraints, the reliable peaks were those 2D Target analytes, reported with corresponding CAS registry number, were identified according to criteria of spectral similarity (DMF above 900 and RMF above 950) and I T tolerance of ±15 units. Analytes are listed with retention times ( 1 t R , 2 t R ) and corresponding precision data expressed as %RSD across all analyses (n = 35), experimental linear retention index (I T ), and tabulated I T (NIST database https://webbook.nist.gov/chemistry/ ), Fisher ratio (F) values calculated for all classes (F all). When features were invariant (e.g., undetected) within a class, the Fisher ratio cannot be computed and in the table is reported as "ND".
peaks that positively matched all but one of the chromatograms (i.e., the most-constrained condition option). The last step of the process was that of targeting the informative compounds, and 269 putatively identified analytes were included. 27 Table 2 lists the target analytes, identified on the 70 eV spectra, according to spectral similarity criteria (DMF above 900 and RMF above 950) and an I T tolerance of ±10 units. The analytes are listed according to their chemical classes together with their CAS registry number, 1 t R , 2 t R , and experimental I T s. Figure 1A−D shows the contour plots of the herbage and fermented LOW DM samples (i.e., Figure 1A herbage; Figure 1B control CON; Figure 1C Lpar; Figure  1D Lbuc).
Composite Class Image Fingerprinting. The composite class image fingerprinting, adopted to promptly highlight any pattern differences between the sample classes, is an automated procedure that was designed in a previous study to detect hazelnut spoilage volatiles. 28 In this procedure, 2D chromatograms, preprocessed and elaborated by means of UT fingerprinting (see section Untargeted Targeted Fingerprinting by Pattern Recognition), are grouped according to their sample class and combined in composite chromatograms from raw silage samples with the three fermented additional classes (i.e., Lpar, Lbuc, and CON) that are representative of each class.
Composite images were generated for all the sample classes (n = 4), and they included both biological (n = 2) and analytical (n = 2) replicates. The composite images resulting from this step of the process were then adopted for a comparative visualization (i.e., visual feature fingerprinting 14 ) to track any differences in pattern between classes. The UT template built according to the procedure described in the experimental section, which included reliable untargeted and targeted peaks, was matched with composite class chromatograms and UT peak responses for further processing.
Data Acquisition, 2D Data Processing, and Statistical Analysis. Raw chromatographic data were acquired by TOF-DS software (Markes International) and processed by GC Image V2020 r1.2 suite (GC Image, LLC).
Heatmap visualization and Hierarchical Clustering (HC) were conducted using Gene-E (https://software.broadinstitute.org/GENE- An unpaired t-test was used to compare the effect of DM level (LOW or HIGH) on the mean values of samples before ensiling. The fermentative characteristics, microbial counts, chemical characteristics, and aerobic stability of silages were analyzed by means of a twoway analysis of variance in a completely randomized design. The used statistical model was as follows: Y ijk = μ + α i + β j + αβ ij + ε ijk , where Y ijk = observation, μ = overall mean, α i = DM level fixed effect (i = LOW or HIGH), β j = inoculum fixed effect (j = CONT or Lbuc or Lpar), αβ ij = interaction effect, and ε ijk = error. The analyses were performed using the R software (R ver. 4.0.3). When the calculated values of F were significant, the Bonferroni posthoc test (P < 0.05) was used to interpret any significant differences among the mean values.  Table 1. The early seeded maize (HIGH) presented a higher DM content and a lower nitrate content than the late seeded maize (LOW). This is representative of the agronomic practices of Northern Italy and represents the range of DM content commonly found in corn silages. The microbial counts of the lactic acid bacteria, yeast, mold, and enterobacteria were also greater in the HIGH treatment than in the LOW one. Table 1 also reports the main fermentative products, microbiological counts, DM losses, and aerobic stability of the silages after 229 d of anaerobic conservation. The LOW silages presented a lower pH in the CON silages than in the Lbuc and Lpar treatments, with the highest value for Lpar. Nitrates were reduced from ensiling and resulted below the detection limit in the Lpar treatment. Lactic acid bacteria (LAB) were higher in Lbuc and Lpar than in the CON silages, whereas yeast, mold, and enterobacteria were close to or below the detection limit, with some differences between treatments. The DM losses were the highest for Lpar and the lowest for CON for both DM contents, whereas the aerobic stability was greatest for Lpar and lowest for CON and Lbuc.
The fermentation patterns of the silages presented different lactic and acetic acid contents between treatments, which in turn affected the lactic-to-acetic ratio. On one hand, 1,2propanediol, a marker of heterolactic fermentation by Lentilactobacillus genus bacteria, was present in CON and Lbuc, whereas it was below the detection limit in the Lpar LOW DM silages. On the other hand, Lpar showed a larger amount of 1-propanol and propionic acid than CON and Lbuc, likely attributable to a secondary degradation of 1,2-propandiol into propionic acid and 1,2-propanol, as has also been witnessed for the metabolic activity of Len. diolivorans in silage. 29 Ethanol was present in all the silages, with amounts ranging from 11.56 to 15.21 g kg −1 DM, without any meaningful variations between the LOW and HIGH DM silages.
Compositional Complexity of the Volatilome and the Major Chemical Classes. The detectable volatilome of the maize silage was the result of multiple and concurrent chemical reactions catalyzed by endogenous and exogenous enzymes from native and inoculated microorganisms and of the environmental conditions that changed during fermentation, due to anaerobiosis, acidification (from a pH of around 6.0 at ensiling to 3.6 and 4.4), and to temperature increases in the first days of ensiling (from 20 to 28°C). The comprehensive capturing of compositional changes (qualitative and quantitative) between samples enabled an accurate evaluation to be made on the differential impact of fermentation and also suggested the predominance of specific metabolic pathways.
The chemical complexity encrypted in the volatilome of the samples included several chemical classes that are closely correlated with known metabolic pathways triggered by the microbial fermentation of primary and specialized plant metabolites. Of the 269 putatively identified components, 72 of them were esters, 41 alcohols, 37 aldehydes, 22 ketones, and 12 were carboxylic acids. The congeners are listed according to their retention order in polar columns in Table 2. Additional classes of interest, because of their biological role, are aromatic derivatives (n = 31) and terpenes/terpenoids (n = 25). The strong signature of lignocellulosic material oxidation is evident within the aromatic derivatives, with several phenol and methoxyphenol derivatives. The presence of terpenes/ terpenoids, specialized plant metabolites, comes from the plant at harvesting. They are here represented by native monoterpenoids (e.g., limonene, linalool, α-terpineol, menthol, geraniol, p-cymene, neral, geranial) and sesquiterpenoids (e.g., α-humulene, β-bisabolene, β-caryophyllene), some characteristic oxidation products (e.g., cis-and trans-linalool oxide, nerol oxide, theaspiranes), and nor-isoprenoids (e.g., β-damascenone, β-ionone, and dihydro-β-ionone). The latter class was likely formed by the oxidative degradation of carotenoids, which usually occurs as a result of enzymatic catalysis.
Terpenes are within the few groups of molecules that are not altered during the digestion of ruminants and which are partially carried over into milk and dairy products, thus giving such products a signature that impacts the sensorial response to milk and cheeses and connects the product to the diet fed to cows. 30 In this study, some terpenes/terpenoids remained almost unchanged after the fermentation process, whereas some others, which had not been detected in the herbages prior to ensiling, appeared after fermentation [e.g., βdamascenone, (E)-theaspirane, α-terpineol, linalool, β-ionone, neral, and nerol-oxide] or even decreased (e.g., limonene, pcimene, geranylacetone, β-bisabolene, neophytadiene, menthol, β-cyclosativene, β-caryophyllene), thus suggesting a microbial action on native substrates that in some cases was differentiated by the inocula (i.e., Lpar mostly degraded cis-linalool oxides).
The high relative amounts of short-chain free fatty acids (lactic, acetic, propionic, and decanoic acids) and alcohols (i.e., ethanol, 1,2-propanediol) produced by fermentation, accom-Journal of Agricultural and Food Chemistry pubs.acs.org/JAFC Article panied by the acidic conditions and temperature increases (to 28°C after 3 d of fermentation) during the first phase of ensiling, help explain the large number of esters (n = 72 congeners) found in all the treatments (see Table 2). The presence of lactate and acetate esters in grass and maize silages was also reported by Weiss, Kroschewski, Auerbach, 8 and Weiss. 31 Interestingly, the ligninocellulosic fiber degradation products formed by oxidative processes on phenolic acids, such as 4propylguaiacol (dihydroeugenol), 2-methoxy-4-methylphenol (p-cresol), 1,3-dimethoxybenzene (3-methoxyanisole), and 4vinylguaiacol, which were below the method's detection limit in the herbage prior to fermentation, were up-modulated by all the treatments for both the LOW and HIGH DM silages. Mishra et al. reported the formation of 4-vinylguaiacol from ferulic acid by yeasts and Bacillus species during beer and wine fermentation. 32 Moreover, some aromatic esters (i.e., methyl benzoate, methyl salicylate, and congeners) with known antifungal and antimicrobic activity increased in the silages after fermentation. Such esters have been used in some studies as silage additives to improve aerobic stability (e.g., Da Silva Pinto 29 ).
Unrevealing the Diagnostic Patterns of Volatiles and Their Correlation with Inocula. In order to delineate the existing strong correlations between the multiple chemical dimensions within the VOCs patterns, a Pearson correlation test was conducted on response data from all the UT features showing meaningful variations within sample classes (Fisher ratio F crit (4, 8) = 3.84 with α = 0.5). Therefore, all the UT features with F calc > 4 were included. The resulting correlation matrix (157 features × 35 samples) is rendered as a heat map in Supporting Information Figure 1 − SF1. Hierarchical clustering, applied to both columns and rows, helps to highlight patterns with strong intercorrelations within samples. As expected, the ester derivatives showed strong correlation (r ≥ 0.900 and significance level α = 0.05) with their respective acid/alcohol moieties. These correlations are highlighted with black squares in Supporting Information Figure 1   on primary plant metabolites dominates the native volatile signatures to a great extent.
The primary interest of this study was to delineate volatile patterns pertaining to specific fermentation microorganisms, and a first unsupervised exploration of the complete data matrix was therefore run by means of principal component analysis (PCA). The considered data included absolute responses from UT components (452 × 35) (component features × samples), and mean centering and normalization were performed prior to PCA elaboration. The obtained results, shown in the score plot on PC1 versus PC2 in Figure 2, suggest a clear impact of the fermentation microorganisms on the total detectable volatilome. The natural sample groups are clustered almost independently, as also shown by the confidence ellipses (95% of confidence level), and correspond to herbage at ensiling (HIGH DM and LOW DM blue indicators) and to fermented silage samples (Lpar pink, Lbuc purple, and CON green indicators) discriminated along the PC1 (35.23% of the total explained variance). In the fermented samples, which in turn were clustered independently along the PC2 (12.71% of the total variance), Lpar was connoted by a distinctive pattern of volatiles, compared to the Lbuc and CON samples, which likely overlapped. The PCA elaboration clearly shows that the fermetative profiles of CON and Lbuc silages were similar, thus suggesting that the control (CON) underwent heterolactic fermentation, especially for the LOW silages (data not shown). This evidence is in keeping with the absence of statistical differences in aerobic stability between the CON and Lbuc in the LOW silages (Table 1). It should be noted that the aerobic stability values were greater than those of the homolactic fermented maize silages (with a lactic-toacetic acid ratio >4.5) with a similar conservation time, which generally ranged from 25 to 95 h of aerobic stability. 3,33 The set of UT features was then sieved to extract those with a statistically relevant variability between all the sample classes. The criterion was driven by the Fisher test; those with an F calc < F crit ; with F crit (4, 8) = 3.84 (α = 0.5) were excluded from any further computations. The F calc values for all the UT features are reported in Supporting Information Table 2.
The herbage at ensiling was also characterized by a distinctive xylene pattern (1,4-dimethyl benzene/p-xylene, and 1,3-dimethyl benzene/m-xylene); their presence, which has never before been reported in studies focused on silage VOCs, deserves further investigations to exclude the possibility of environmental contamination. Their reduction in silages, where their relative abundance was on average 0.3 to 0.6 of that of the corresponding herbage prior to fermentation, is of particular interest.
Lpar has a strong and distinctive signature that is dominated by propionic acid and 1-propanol; characteristic volatiles, as also confirmed by the quantitative HPLC data (Table 1), form an independent cluster in Figure 3 (purple square). Several corresponding esters can be observed: ethyl propionate, propyl propionate, propyl palmitate, isoamyl propionate, and ethyl nonanoate. The predominance of propionic esters is in keeping with the existing knowledge on silage volatile signatures, as reported by Hafner et al. 9 and Lee et al. 31 in whole oat flour fermented by Lcb. paracasei. However, the large chemical diversity of the detected esters far exceeds the previously documented diversity.
The impact of Len. Buchneri fermentation, which generated a volatile pattern that is not clearly distinguishable from the CON samples, is instead connoted by the presence of several ethyl esters (i.e., ethyl propionate, ethyl nonanoate, ethyl palmitate, ethyl dodecanoate, and ethyl benzoate). This suggests that a relevant amount of ethanol produced from fermentation led to ester formation and was favored by the presence of organic acids in acidic conditions. The presence of acetic acid and acetates (i.e., benzyl acetate and heptyl acetate), whose formation is coherent with the heterolactic fermentation acted by bacteria of Lentilactobacillus genus, 35 is also relevant.
Visual Feature Fingerprinting Used to Promptly Highlight VOCs Diagnostic Patterns. The successive data elaboration step was aimed at highlighting the distinctive fermentation patterns induced by lactic acid bacteria on herbage at ensiling, obtained by means of visual feature f ingerprinting. 14 The approach performs a pairwise image comparison, through the use of a dedicated algorithm that computes the difference for each data point (i.e., the output of the detector at a point in time) between pairs of chromatograms. These differences are then mapped in a Hue-Intensity-Saturation (HIS) color space to create an image of the relative differences between image pairs in the retention-times plane. 36 The procedure is fully automated, and when applied after UT fingerprinting, it provides information about variations in targeted or untargeted features between pairwise compared samples. In this application, visual comparisons were performed on composite class images 28 generated by combining 2D chromatograms from samples belonging to the class (e.g., herbage at ensiling, Lbuc, Lpar, control CON). Details of the application can be found in the experimental section.
The example in Supporting Information Figure 2A − SF2A refers to an Lpar composite-class image and was considered as the analyzed class image compared with the raw silage considered as the reference. The resulting image is rendered as a "colorized fuzzy ratio"; the difference between the aligned class images is computed at each data point and colored green when positive (larger detector response in the analyzed image, i.e., samples fermented by Lcb. paracasei) or red when the difference is negative (larger detector response in the reference image, i.e., unfermented herbage). The brightness in the image depends on the magnitude of the response; white saturation indicates pixels/regions with detector responses that are nearly equal to the pair images.
The data point differences (red or green colored pixels) combined with response data from the UT features indicate that several analytes, including some untargeted ones, were likely, if not exclusively, produced by Lcb. paracasei fermentation and were not detectable in the herbage prior to fermentation. These volatile metabolites may be considered the result of a specific transformation pathway of Lcb. paracasei. The most abundant volatiles were: propyl acetate, isoamyl lactate, ethyl lactate, propyl pentanoate, propyl phenylacetate, and propyl laurate. Degradation products of phenolic acids were also formed: 2-methoxy-4-propylphenol (isoeugenol), 4propylguaiacol, and p-cresol. These components, connoted by a strong phenolic odor, could have an impact on the sensory properties of silage but could also exert a protective effect against molds such as Aspergillus parasiticus. 37 A selection of volatiles already detected in the herbage prior to ensiling, but largely up-regulated by Lcb. paracasei fermentation, are shown in the histogram in Figure 4A. Analytes, grouped according to their chemical classes (differently colored histogram bars), are reported in descending order of the response ratios (i.e., the ratio between the absolute 2D peak volumes). For all these components, F calc > F crit .
Propanoic acid (F calc 247) was the most variable organic acid, with a 46-fold increase over the herbage at ensiling; accordingly, 3-methyl-1-butyl propanoate (isoamyl propionate − F calc 313) was 45 times more abundant in the fermented samples. Furfuryl alcohol (2-furanmethanol − F calc 101) showed a 15-fold increase, while 1-propanol (F calc 907) had a 9-fold increase. Ethyl nonanoate (F calc 19) was 219 times higher in Lcb. paracasei samples than in the herbage prior to fermentation. Nonanoic acid and its ethyl ester have already been found in Lcb. paracasei fermentation products. 34,38 The comparative visualization shown in Supporting Information Figure 2B − SF2B highlights pattern differences between composite-class images obtained by combining 2D chromatograms of Lbuc silage samples versus unfermented herbage. An inspection of the response data and the relative supervised statistics (Fisher ratio Lbuc vs herbage prior to ensiling) indicates that several targeted compounds in the fermented silages were below the method's detection limit. Such compounds could be considered characteristic yet unique components of unfermented samples. Among these compounds, several carbonylic derivatives were identified: (Z)-2hexenal; (E)-3-hexenal; (E,E)-2,4-nonadienal, (Z)-2-undecenal; pentadecanal; 2-propanone; 2-butanone; 2-pentanone; (E,E)-3,5-octadien-2-one; 2-nonanone; 2-undecanone. Their presence suggests an extensive lipoxygenase hydroperoxy liase activity, likely as the result of the activity of plant endogenous enzymes. Some of the compounds were also in the subgroup of most informative components for maize herbage prior to fermentation, as shown in Figure 3.
Several analytes were also detected in the fermented samples, and those with the highest responses were: propyl acetate; diethyl succinate; ethyl sorbate; ethyl lactate; isoamyl lactate, and p-cresol. Lactic and acetic acid esters, derived from esterification of the main organic acids produced by heterolactic fermentation, 39 were the most abundant esters and showed the highest percentage of the total response. As far as the statistically meaningful variations (F calc > F crit ) are concerned, the histogram in Figure 4B illustrates the foldchange of a selection of targeted compounds ordered according to their chemical class. Propionic (F calc 147) and pentanoic acid (F calc 12) resulted to be 18 and 7 times more abundant, respectively, in silage fermented by Lbuc than in the original herbage. The larger amount of several aromatic derivatives, as also discussed in section "Compositional Complexity of the Volatilome and the Major Chemical Classes", could have beneficial effects on the microbial and yeast stability of silage. Isoeugenol, guaiacol, 2,3-dimethylphenol, methyl salicylate, and benzeneacetaldehyde, some of the most up-regulated phenols, are worth mentioning. In particular, isoeugenol had a 22-fold increase, compared to the original herbage. Eugenol and its derivatives are known to have antimicrobial activity against yeast (i.e., Saccharomyces cerevisiae) 40 and the fungi of Aspergillus genus, 37 with a lower minimal inhibitory concentration for iso-eugenol. Moreover, eugenol and its derivatives have also been studied because of their inhibition effect on the capacity of molds to produce mycotoxins. 41,42 Those with a higher statistical relevance on average showed a 20-fold increase for ethyl esters and dominated the others (e.g., ethyl linoleate 59-fold; ethyl nonanoate 50-fold; and ethyl caprate 43-fold increase).
The different impact of Lcb. paracasei fermentation over the control samples, where epiphytic microbiota dominated fermentation, is shown in Supporting Information Figure 2C − SF2C, where composite-class images of Lcb. paracasei samples are compared with cumulative chromatographic patterns of control samples. The observed compositional differences can mainly be ascribed to the inoculation of Lcb. paracasei, which affected fermentation through a strongly heterolactic pathway with a relevant production of acetic acid, propionic acid, and 1-propanol ( Table 1). The latter formed from the secondary degradation of acetic acid and 1,2propandiol caused by such bacteria of the Lentilactobacillus genus as Len. Diolivorans. 29 The volatiles that show the most meaningful variation between Lpar and CON samples (shown in the histogram in Figure 4C) are propionic acid, with an eightfold increase (F calc 150), followed by 1-propanol (sixfold increase), 2-butanol (fourfold increase), and 3-methyl-2-butanol (threefold increase). The esters, which are dominated by 1-propanol derivatives, on average show a 10-fold increase. Compared to control samples, the silage volatilome impacted by Lcb. paracasei shows a distinctive signature dominated by propionic acid and 1-propanol derivatives.
The great chemical complexity of fermented silage VOCs, explored by GC × GC-TOF MS, adds knowledge on the metabolic pathways triggered by specific bacterial strains and offers further interpretation keys that can be used to obtain a better understanding of silage stabilization mechanisms against the degradative action of yeasts and molds during the exposure of silage to air. This study, by combining well-established chemical characterization protocols and marker compound monitoring, has enabled a robust cross-validation of data derived from comprehensive VOCs fingerprinting, based on HS-SPME sampling. Although not quantitative per se, the approach promptly reveals pattern variations over a large dynamic range of concentrations and provides evidence on the activation of metabolic pathways and on the synergies between bacterial strains.
Moreover, by resorting to untargeted/targeted fingerprinting, it was possible to monitor several chemical classes, while the up-/down-regulation of single analytes was easily tracked over many samples by multivariate statistics and dedicated data processing (i.e., visual feature fingerprinting).
Metadata [retention times 1 t R , 2 t R , corresponding precision data as %RSD (n = 35), and experimental linear retention index (I T )], average response over samples' classes, and precision data (% RSD) on QCs features volumes; Target components and untargeted features mapped through all analyzed samples. Target analytes, reported with corresponding CAS registry number, were identified according to criteria of spectral similarity (DMF above 900 and RMF above 950) and I T tolerance of ±15 units. Analytes are listed with retention times ( 1 t R , 2 t R ) and corresponding precision data expressed as %RSD across all analyses (n = 35), experimental linear retention index (I T ) and tabulated I T (NIST database https://webbook.nist.gov/chemistry/ ), Fisher ratio (F) values calculated for all classes (F all). When features were invariant (e.g., undetected) within a class, the Fisher ratio cannot be computed and in table is reported as "ND". This table complements Table 2 of the manuscript. Pearson correlation matrix obtained from absolute response data corresponding to UT features with a F calc >4 for all classes. Hierarchical clustering is based on Pearson correlation while heat map colorization ranges from blue (−1) to red (1) r values. Black squares highlight features cluster with a strong correlation. Comments are reported in the text; Comparative visualization between composite class images obtained by summing 2D chromatograms from samples belonging to the same class. In SF2A the analyzed image is the composite-class chromatogram from L. par fermented samples while as reference is taken the composite class image from all herbage samples. In SF2B the analyzed image is from L. buc while the reference is that from herbage samples. In SF2C the analyzed is that from L par samples compared to the reference from control samples. The comparative visualization is rendered as "colorized fuzzy ratio"; the difference at each data point between aligned pairwise images is computed and colored green, when positive (larger detector response in the analyzed image) or colored red, when negative (larger detector response in the reference image) (PDF)